Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method

Copper (1) oxide nanoparticles together with matrix polymers of polyvinyl alcohol (PVA) and polyaniline (PANI) composite films were synthesized, as these materials are of importance in optoelectronic applications. ‎Nanoparticles of Cu₂O were produced by chemical precipitation. Polymerization of aniline was carried out through polymerization in an acidic medium. Structural, thermal, and optical properties of PVA+PANI/Cu₂O nanocomposite were inspected by x-ray diffraction (XRD), scanning electron microscopy (SEM), fourier-transform infrared (FTIR), differential scanning calorimeter (DSC) and ultraviolet-visible spectroscopy (UV-Vis spectroscopy). X-ray diffraction peaks at 29.53°, 36.34°, and 42.22° indicated the

     The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, an

  In this research we study one of the pollutants(Gelbstoff ) such as Humic  and Fulvic Acids  in tap waters by using the technique of  Raman, Flora to some regions of Baghdad , the results appear that the tap waters  were pollutants which know yellow substance or Gelbstoff instant of suspending waters, which appear through the scattering of the incident light to same the wave length of Raman ,also calculate Raman shift which was 3640 cm-1 and force constant to band (O – H ) was 743 N/m, where the peak of Raman was at the wave length 441 nm after used the excitation wave length 380 nm . The results were in an agreement with lectures [8][9][10].

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121